



import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class      _UpperCAmelCase      :




     def __init__(					self			,    a__			,    a__=1_3			,    a__=7			,    a__=True			,    a__=True			,    a__=False			,    a__=True			,    a__=9_9			,    a__=3_2			,    a__=5			,    a__=4			,    a__=3_7			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=1_6			,    a__=2			,    a__=0.0_2			,    a__=3			,    a__=4			,    a__=None			,    ):
         A__					=       parent
         A__					=       batch_size
         A__					=       seq_length
         A__					=       is_training
         A__					=       use_input_mask
         A__					=       use_token_type_ids
         A__					=       use_labels
         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       intermediate_size
         A__					=       hidden_act
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       max_position_embeddings
         A__					=       type_vocab_size
         A__					=       type_sequence_label_size
         A__					=       initializer_range
         A__					=       num_labels
         A__					=       num_choices
         A__					=       scope




     def 		snake_case_		(					self):
         A__					=       ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size)

         A__					=       None
         if self.use_input_mask:
             A__					=       random_attention_mask([self.batch_size, self.seq_length])

         A__					=       None
         if self.use_token_type_ids:
             A__					=       ids_tensor([self.batch_size, self.seq_length]			,    self.type_vocab_size)

         A__					=       None
         A__					=       None
         A__					=       None
         if self.use_labels:
             A__					=       ids_tensor([self.batch_size]			,    self.type_sequence_label_size)
             A__					=       ids_tensor([self.batch_size, self.seq_length]			,    self.num_labels)
             A__					=       ids_tensor([self.batch_size]			,    self.num_choices)

         A__					=       self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




     def 		snake_case_		(					self):
         return OpenLlamaConfig(
             vocab_size=self.vocab_size			,    hidden_size=self.hidden_size			,    num_hidden_layers=self.num_hidden_layers			,    num_attention_heads=self.num_attention_heads			,    intermediate_size=self.intermediate_size			,    hidden_act=self.hidden_act			,    hidden_dropout_prob=self.hidden_dropout_prob			,    attention_probs_dropout_prob=self.attention_probs_dropout_prob			,    max_position_embeddings=self.max_position_embeddings			,    type_vocab_size=self.type_vocab_size			,    is_decoder=a__			,    initializer_range=self.initializer_range			,    use_stable_embedding=a__			,    )




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       OpenLlamaModel(config=a__)
         model.to(a__)
         model.eval()
         A__					=       model(a__			,    attention_mask=a__)
         A__					=       model(a__)
         self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    ):
         A__					=       True
         A__					=       OpenLlamaModel(a__)
         model.to(a__)
         model.eval()
         A__					=       model(
             a__			,    attention_mask=a__			,    encoder_hidden_states=a__			,    encoder_attention_mask=a__			,    )
         A__					=       model(
             a__			,    attention_mask=a__			,    encoder_hidden_states=a__			,    )
         A__					=       model(a__			,    attention_mask=a__)
         self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    ):
         A__					=       OpenLlamaForCausalLM(config=a__)
         model.to(a__)
         model.eval()
         A__					=       model(a__			,    attention_mask=a__			,    labels=a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    ):
         A__					=       True
         A__					=       True
         A__					=       OpenLlamaForCausalLM(config=a__)
         model.to(a__)
         model.eval()

         # first forward pass
         A__					=       model(
             a__			,    attention_mask=a__			,    encoder_hidden_states=a__			,    encoder_attention_mask=a__			,    use_cache=a__			,    )
         A__					=       outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         A__					=       ids_tensor((self.batch_size, 3)			,    config.vocab_size)
         A__					=       ids_tensor((self.batch_size, 3)			,    vocab_size=2)

         # append to next input_ids and
         A__					=       torch.cat([input_ids, next_tokens]			,    dim=-1)
         A__					=       torch.cat([input_mask, next_mask]			,    dim=-1)

         A__					=       model(
             a__			,    attention_mask=a__			,    encoder_hidden_states=a__			,    encoder_attention_mask=a__			,    output_hidden_states=a__			,    )['''hidden_states'''][0]
         A__					=       model(
             a__			,    attention_mask=a__			,    encoder_hidden_states=a__			,    encoder_attention_mask=a__			,    past_key_values=a__			,    output_hidden_states=a__			,    )['''hidden_states'''][0]

         # select random slice
         A__					=       ids_tensor((1,)			,    output_from_past.shape[-1]).item()
         A__					=       output_from_no_past[:, -3:, random_slice_idx].detach()
         A__					=       output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(a__			,    a__			,    atol=1e-3))




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         (
             (
             A__							
         )			,						(
             A__							
         )			,						(
             A__							
         )			,						(
             A__							
         )			,						(
             A__							
         )			,						(
             A__							
         )			,						(
             A__							
         )			,													
         )					=       config_and_inputs
         A__					=       {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict







@require_torch
class      _UpperCAmelCase      (							A__      ,   A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					(
         (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
     )
     UpperCamelCase__		     =					(OpenLlamaForCausalLM,) if is_torch_available() else ()
     UpperCamelCase__		     =					(
         {
             '''feature-extraction''': OpenLlamaModel,
             '''text-classification''': OpenLlamaForSequenceClassification,
             '''text-generation''': OpenLlamaForCausalLM,
             '''zero-shot''': OpenLlamaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					False




     def 		snake_case_		(					self):
         A__					=       OpenLlamaModelTester(self)
         A__					=       ConfigTester(self			,    config_class=a__			,    hidden_size=3_7)




     def 		snake_case_		(					self):
         self.config_tester.run_common_tests()




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             A__					=       type
             self.model_tester.create_and_check_model(*a__)




     def 		snake_case_		(					self):
         A__			,						A__					=       self.model_tester.prepare_config_and_inputs_for_common()
         A__					=       3
         A__					=       input_dict['''input_ids''']
         A__					=       input_ids.ne(1).to(a__)
         A__					=       ids_tensor([self.model_tester.batch_size]			,    self.model_tester.type_sequence_label_size)
         A__					=       OpenLlamaForSequenceClassification(a__)
         model.to(a__)
         model.eval()
         A__					=       model(a__			,    attention_mask=a__			,    labels=a__)
         self.assertEqual(result.logits.shape			,    (self.model_tester.batch_size, self.model_tester.num_labels))




     def 		snake_case_		(					self):
         A__			,						A__					=       self.model_tester.prepare_config_and_inputs_for_common()
         A__					=       3
         A__					=       '''single_label_classification'''
         A__					=       input_dict['''input_ids''']
         A__					=       input_ids.ne(1).to(a__)
         A__					=       ids_tensor([self.model_tester.batch_size]			,    self.model_tester.type_sequence_label_size)
         A__					=       OpenLlamaForSequenceClassification(a__)
         model.to(a__)
         model.eval()
         A__					=       model(a__			,    attention_mask=a__			,    labels=a__)
         self.assertEqual(result.logits.shape			,    (self.model_tester.batch_size, self.model_tester.num_labels))




     def 		snake_case_		(					self):
         A__			,						A__					=       self.model_tester.prepare_config_and_inputs_for_common()
         A__					=       3
         A__					=       '''multi_label_classification'''
         A__					=       input_dict['''input_ids''']
         A__					=       input_ids.ne(1).to(a__)
         A__					=       ids_tensor(
             [self.model_tester.batch_size, config.num_labels]			,    self.model_tester.type_sequence_label_size).to(torch.float)
         A__					=       OpenLlamaForSequenceClassification(a__)
         model.to(a__)
         model.eval()
         A__					=       model(a__			,    attention_mask=a__			,    labels=a__)
         self.assertEqual(result.logits.shape			,    (self.model_tester.batch_size, self.model_tester.num_labels))




     @unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''')
     def 		snake_case_		(					self):
         pass




     @parameterized.expand([('''linear''',), ('''dynamic''',)])
     def 		snake_case_		(					self			,    a__):
         A__			,						A__					=       self.model_tester.prepare_config_and_inputs_for_common()
         A__					=       ids_tensor([1, 1_0]			,    config.vocab_size)
         A__					=       ids_tensor([1, int(config.max_position_embeddings * 1.5)]			,    config.vocab_size)

         set_seed(4_2)  # Fixed seed at init time so the two models get the same random weights
         A__					=       OpenLlamaModel(a__)
         original_model.to(a__)
         original_model.eval()
         A__					=       original_model(a__).last_hidden_state
         A__					=       original_model(a__).last_hidden_state

         set_seed(4_2)  # Fixed seed at init time so the two models get the same random weights
         A__					=       {'''type''': scaling_type, '''factor''': 1_0.0}
         A__					=       OpenLlamaModel(a__)
         scaled_model.to(a__)
         scaled_model.eval()
         A__					=       scaled_model(a__).last_hidden_state
         A__					=       scaled_model(a__).last_hidden_state

         # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
         # maximum sequence length, so the outputs for the short input should match.
         if scaling_type == "dynamic":
             self.assertTrue(torch.allclose(a__			,    a__			,    atol=1e-5))
         else:
             self.assertFalse(torch.allclose(a__			,    a__			,    atol=1e-5))

         # The output should be different for long inputs
         self.assertFalse(torch.allclose(a__			,    a__			,    atol=1e-5))






'''simple docstring'''


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase_					=   logging.getLogger()


def     SCREAMING_SNAKE_CASE							(   a_						:				Path					,       a_						:				list      ):
  __a								=       '\n'.join(a_      )
  Path(a_      ).open('w'      ).writelines(a_      )


UpperCAmelCase_					=   "patrickvonplaten/t5-tiny-random"
UpperCAmelCase_					=   "sshleifer/bart-tiny-random"
UpperCAmelCase_					=   "sshleifer/tiny-mbart"

UpperCAmelCase_					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class  __lowercase    (	__magic_name__			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()
     __a								=       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
     _dump_articles(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       str(Path(self.get_auto_remove_tmp_dir()   ) / 'scores.json'   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       run_generate()
       assert Path(UpperCamelCase   ).exists()
       # os.remove(Path(output_file_name))







   def 		UpperCamelCase__      (						self   )					->     int:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([BART_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Union[str, Any]:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([T5_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()

     __a								=       {
         'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
         'de': [
             'Maschinelles Lernen ist großartig, oder?',
             'Ich esse gerne Bananen',
             'Morgen ist wieder ein toller Tag!',
         ],
     }

     __a								=       Path(self.get_auto_remove_tmp_dir()   )
     __a								=       str(tmp_dir / 'scores.json'   )
     __a								=       str(tmp_dir / 'val.target'   )
     _dump_articles(UpperCamelCase   ,				text['en']   )
     _dump_articles(UpperCamelCase   ,				text['de']   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {str(UpperCamelCase   )}\n            {str(UpperCamelCase   )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
     testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']   )

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       with CaptureStdout() as cs:
         run_search()
       __a								=       [' num_beams | length_penalty', model, 'Best score args']
       __a								=       ['Info']
       if "translation" in task:
         expected_strings.append('bleu'   )
       else:
         expected_strings.extend(UpperCamelCase   )
       for w in expected_strings:
         assert w in cs.out
       for w in un_expected_strings:
         assert w not in cs.out
       assert Path(UpperCamelCase   ).exists()
       os.remove(Path(UpperCamelCase   )   )



